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1. Supportive Negatives Spectral Augmentation for Source-Free Cross-Domain Segmentation

   https://doi.org/10.1609/aaai.v39i10.33148  

   Zheng, Kexin; Xia, Haifeng; Xia, Siyu; Shao, Ming; Ding, Zhengming (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method `supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which source model produces incorrect predictions. And, active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage source model to gradually capture and adapt the attributions of target distribution. Considerable experimental studies demonstrate that annotating merely 4%~5% of negative instances from the target domain significantly improves segmentation performance over previous methods. 

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2. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

   Zhu, Jun-Yan; Park, Taesung; Isola, Phillip; Efros, Alexei A (October 2017, IEEE International Conference on Computer Vision)

   Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $$X$$ to a target domain $$Y$$ in the absence of paired examples. Our goal is to learn a mapping $$G: X \rightarrow Y$$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $$Y$$ using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping $$F: Y \rightarrow X$$ and introduce a {\em cycle consistency loss} to push $$F(G(X)) \approx X$$ (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach. 

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3. Non-IID Transfer Learning on Graphs

   https://doi.org/10.1609/AAAI.V37I9.26231  

   Wu, Jun; He, Jingrui; Ainsworth, Elizabeth (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks. 

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4. Low-Dimensional Density Ratio Estimation for Covariate Shift Correction

   Petar Stojanov, Mingming Gong (April 2019, Proceedings of Machine Learning Research)

   Covariate shift is a prevalent setting for supervised learning in the wild when the training and test data are drawn from different time periods, different but related domains, or via different sampling strategies. This paper addresses a transfer learning setting, with covariate shift between source and target domains. Most existing methods for correcting covariate shift exploit density ratios of the features to reweight the source-domain data, and when the features are high-dimensional, the estimated density ratios may suffer large estimation variances, leading to poor performance of prediction under covariate shift. In this work, we investigate the dependence of covariate shift correction performance on the dimensionality of the features, and propose a correction method that finds a low-dimensional representation of the features, which takes into account feature relevant to the target Y, and exploits the density ratio of this representation for importance reweighting. We discuss the factors that affect the performance of our method, and demonstrate its capabilities on both pseudo-real data and real-world applications. 

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5. Towards Testing Monotonicity of Distributions Over General Posets

   Alaikbarpour, A.; Gouleakis, T.; Peebles, J.; Rubinfeld, R.; Yodpinyanee, A (January 2019, Proceedings of the Thirty-Second Conference on Learning Theory (COLT 2019))

   In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution p over a poset is monotone if, for any pair of domain elements x and y such that x ⪯ y, p(x) ≤ p(y). To understand the sample complexity of this problem, we introduce a new property called bigness over a finite domain, where the distribution is T-big if the minimum probability for any domain element is at least T. We establish a lower bound of Ω(n/ log n) for testing bigness of distributions on domains of size n. We then build on these lower bounds to give Ω(n/ log n) lower bounds for testing monotonicity over a matching poset of size n and significantly improved lower bounds over the hypercube poset. We give sublinear sample complexity bounds for testing bigness and for testing monotonicity over the matching poset. We then give a number of tools for analyzing upper bounds on the sample complexity of the monotonicity testing problem. The previous lower bound for testing Monotonicity of 

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